Year: 2009

Duncan Murdoch just posted a youtube video presenting an animation clip of a 3d rgl object.

Duncan even went further and wrote an explanation on how he made the video:

here are the steps I used:
1. Design a shape to be displayed, and then play with the animation functions to make it change over time. Use play3d to do it live in R, movie3d to write the individual frames of the movie to .png files.
2. Use the ffmpeg package (not an R package, a separate project at http://ffmpeg.org) to convert the .png files to an .mp4 file. The individual frames totalled about 1 GB; the compressed movie is about 45 MB.
3. Upload to Youtube. I’m not a musician, so I had to use one of their licensed background tracks, I couldn’t write my own. I spent a lot of time picking one and then adjusting the timing of the video to compensate. Each render/upload cycle at full resolution took about an hour and a half. It’s a lot faster to render in a smaller window with fewer frames per second, but it’s still tedious. It’s easier to synchronize if you actually have a copy of the music locally, but Youtube doesn’t let you download their music. So the timing isn’t perfect, but it’s good enough for me!

I already wrote about R-bloggers on the R mailing list, so it only seems fitting to write about it more here. I will explain what R-bloggers is and then move to explain what I hope it will accomplish.

R-Bloggers.com is a central hub of content collected from bloggers who write about R (in English) and if you are an R blogger you can join it by filling in this form.

I built the site with the aspiration to help R bloggers and users to connect and follow the “R blogosphere”. When I am writing these words, R-bloggers already has 17 blogs in it, and I hope for many (many) more.

How does R-Bloggers operate? This site aggregates feeds (only with permission!) from participating R blogs. The beginnings of each participating blog’s posts will automatically be displayed on the main page with links to the original posts; inside every post there is a link to the original blog and links to other related articles. While all participating blogs have links in the “Contributors” section of our sidebar

What does R-Bloggers offer it’s visitors?

Discover (for all): Find new R blogs you didn’t know about. And Search in them for content you want.

Follow (for people who don’t use RSS): Enter your e-mail and subscribe to receive a daily digest with teasers of new posts from participating blogs. You will more easily get a sense of hot topics in the R blogosphere.

Connect (for facebook users): Click on “Fan this site” to become a “fan” of R Bloggers. You can then “friend” other people and share thoughts on our wall. Or just by leaving comments on the blog.

Participate (for bloggers): Add your R blog to get increased visibility (for readers and search engines) with permanent links on our Contributors sidebar. Your blog will also gain visibility via our e-mail digest and through your presence on the main page with posts.

Who started R-Bloggers (and way)? R Bloggers was started by Tal Galili (well, me). After searching for numerous R blogs I decided that there must be more R blogs our there then he knows about, and maybe the best way for finding them is to make them find him.

After writing about it in the R mailing list, I got some good feedbacks but also questions about why use only R blogs and not all the R feeds that exist. Who is the website actually for (when there are services like Google reader for us to read our feeds with), and what am I hoping it will do. So here is what I answered:

For me there are two audiences: One is that of the web 2.0 power users. That is, people who know what RSS is and use it, maybe evern write their own blogs. These people have only one problem (as I see it) that R-bloggers tries to solve, and that is to know who else lives in their ecosystem. Who else they should follow.
For that, google reader recommendation system is great, but not enough. A much better system is if there was a one place where all R bloggers would go, write down their website, and all of us would know they exist. That is what R-bloggers offers for the power users. I think this is also why over 20 of them subscribed to the site RSS feed.
BTW, The origin of this idea came to me when I was trying to find all the dance bloggers for my wife (who is a dance researcher and blogger herself). After a while we started http://www.dancebloggers.com/ while knowing of only 10 bloggers. They list now has over 80 bloggers, most of which we would have not known about without this hub.
The same thing I am trying to do for the R community, that is way I hope more R bloggers would write about the service – so their network of readers which includes other R bloggers would add themselves and we will all know about them.
If that was my only purpose, a simple directory would have been enough. But I also have a second one and that is to help the second audience.

The second audience I am thinking of are people of our community who are not so much early adopters (and actually quite late adapters) of the new facilities that the new web (a.k.a: web 2.0) provides.
To them the all RSS thing is too much to look at, and they are used to e-mails. And because of that they are (until now) disconected from many of the R bloggers out there, simply because it is in-efficient for them to go through all these blogs each day (or even week). So for them, to see all the content in one place (and even get an e-mail about it) would be (I hope) a service. I believe that’s why 5 of them (so far) has subscribed via e-mail.
I also hope teachers will direct their students to this as a resource for getting a sense of what people who are using R are doing.
Another thing that hints me about the R community is seeing how the “facebook fan box” is still empty. Which tells me that (sadly) very few R users are actively using facebook as a means for connecting with the outer networks of people out there.

All I wrote also explains why R-bloggers will only take feeds of bloggers and only (as much as can be said) their posts that are centered around R (hence the website name ).
It both follows what Gabor talked about – having a site who’s content is only about R. But also what I wish, which is to have “content” in the sense of articles to read (mostly). And not so much things like news feeds of wikipedia or new packages published.

I hope this post will both notify people about this new resource, encourage more R bloggers to join, and will help for future people to better understand what this R-Bloggers thing is all about

One of the exciting new frontiers for R programming is of creating website interfaces to R code. At the forefront of this domain is a young and (very) bright man called Jeroen Ooms, whom I had the pleasure of meeting at useR 2009 (press the link to see his presentation).

New features include 1D geom’s (histogram, density, freqpoly), syntax mode (by clicking the tiny arrow at the bottom), and some additional facet options. And some minor improvements and fixes, most notably for Internet Explorer.
The data upload has not been improved yet, I am working on that. For now, it supports .csv, .sav (spss), and tab delimited data. Please make sure your filename has the appropriate extension and every column has a header in your data. If you export a dataframe from R, use:
write.csv(mydf, ”mydf.csv” , row.names=F). If you upload an spss
datafile, none of this should be a concern.
Supported browsers are IE6-8, FF, Safari, and Chrome, but a recent browser is highly recommended. As always, feedback is more than welcome.

This post will eventually grow to hold a wide list of books on statistics (e-books, pdf books and so on) that are available for free download. But for now we’ll start off with just one several books:

The Elements of Statistical Learning written by Trevor Hastie, Robert Tibshirani and Jerome Friedman. you can legally download a copy of the book in pdf format from the authors website! Direct download (First discovered on the “one R tip a day” blog)

Introduction to Statistical Thought by Michael Lavine. The book is organized into seven chapters: “Probability,” “Modes of Inference,” “Regression,” “More Probability,” “Special Distributions,” “More Models,” and “Mathematical Statistics.” and makes extensive use of R. Here is a favoring review the book received in JASA. 328 pages. Download link (approx. 40 mbyte)

Using R for Introductory Statistics by John Verzani Publisher: Chapman & Hall/CRC 2004 ISBN/ASIN: 1584884509 ISBN-13: 9781584884507 Number of pages: 114 Description: The author presents a self-contained treatment of statistical topics and the intricacies of the R software. The book treats exploratory data analysis with more attention than is typical, includes a chapter on simulation, and provides a unified approach to linear models. This text lays the foundation for further study and development in statistics using R. Download link

Psychometric Theory with Applications in R by William Revelle (a work in progress) Download link

A great long list of R related texts, for free download, can be found here.

Using Graphs Instead of Tableswebsite link (This web page accompanies the article “Using Graphs Instead of Tables in Political Science”, by Jonathan Kastellec and Eduardo Leoni, which appears in the December 2007 issue of Perspectives on Politics. It contains complete replication code for all the graphs that appear in the text)

Today I noticed a call for R users to gather around a single campfire for one hour and share their questions and answers.

The campfire name is stackoverflow.com, a site dedicated for handling programming questions. The event details are bellow:

From: The R Flashmob Project
Subject: R Flashmob #2

You are invited to take part in R Flashmob, the project that makes the
world a better place by posting helpful questions and answers about the
R statistical language to the programmer’s Q & A site stackoverflow.com

Please forward this to other people you know who might like to join.

FAQ

Q. Why would I want to join an inexplicable R mob?

A. Tons of other people are doing it.

Q. Why else?

A. Stackoverflow was built specifically for handling programming questions.
It’s a better mousetrap. It offers search (and is well indexed by search engines),
tagging, voting, the ability to choose the “best” answer to a question, and the ability to
edit questions and answers as technology progresses. It has a karma system to
reward people who are happy to help and discourage MLJs (mailing list jerks).

Q. Do the organizers of this MOB have any commercial interest in stackoverflow?

A. None at all. We’re just convinced it is the best way to help and promote R. All
the content submitted to stackoverflow is protected by a Creative Commons
CC-Wiki License, meaning anyone is free to copy, distribute, transmit, and
remix the information on stackoverflow. All the content on stackoverflow is
regularly made available for download by the public.

(2) The mob should form at precisely 4 minutes past the hour and not beforehand.

(3) At 4 minutes past the hour, you should arrive at stackoverflow.com, log in,
and post 3 R questions. Be sure to tag the questions “R”. See the posting
guidelines at http://stackoverflow.com/faq to understand what makes a good
question.

(6) During the R MOB, you can chat with other participants on the #R channel
on IRC (freenode). To do this, install the Chatzilla extension on Firefox.
Click “freenode” on the main screen. Then type /join #R in the field at the
bottom of the screen. Then chat.

(7) If you finish posting your three questions within the 50 minutes, stick
around to answer questions and give “up votes” to good questions and answers.

(8) IMPORTANT: After posting, sign the R Flashmob guestbook at

http://bit.ly/6F8B2

(9) Return to what you would otherwise have been doing. Await
instructions for R MOB #3.

I guess this is not the number one post I would like to start with on this blog, but I feel the time is right for it (community-wise).

I’ll move on to the subject matter in a moment, but first a short intro: This blog is written by Tal Galili. I am an aspiring statistician who also loves to use R for his work. At the same time I am also a WordPress blogger, writing mainly at www.TalGalili.com where I can use my native language (Hebrew) for self expression.

This combination of statistics and blogging will lead me to sometimes much less statistical, but more Web/Open-Source oriented posts like this one. So for the statisticians in the audience I extend my apologies and invite you to wait for future posts which will be more fully focused on Statistics and R.

Highlights

R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. If you wish to download R, please choose your preferred CRAN mirror.

The R language has become a de facto standard among statisticians for the development of statistical software,and is widely used for statistical software development and data analysis.

One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

R is available as Free Software under the terms of the Free Software Foundation‘s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

R and S

R is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

The R environment

R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes

an effective data handling and storage facility,

a suite of operators for calculations on arrays, in particular matrices,

graphical facilities for data analysis and display either on-screen or on hardcopy, and

a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.

R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.

Many users think of R as a statistics system. We prefer to think of it of an environment within which statistical techniques are implemented. R can be extended (easily) via packages. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very wide range of modern statistics.

R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hardcopy.